Rafael Data

‘’’{r echo=TRUE, message=FALSE, paged.print=FALSE} library(knitr)

Importing Data

‘’’{r echo=TRUE, message=FALSE, paged.print=FALSE} library(knitr) library(readr) NYCC <- read_csv(‘desktop/NYPD_Crime.csv’) library(dplyr) library(tibble) library(tidyverse) glimpse(NYCC)

Renaming, Selecting & Recoding Variables

‘’’{r echo=TRUE, message=FALSE, paged.print=FALSE} library(knitr) NYCC <- dplyr::rename(NYCC, Crime_Committed = OFNS_DESC, Age_Group = SUSP_AGE_GROUP) NYCC <- select(NYCC, BORO_NM, LAW_CAT_CD, JURIS_DESC, Crime_Committed, Age_Group, SUSP_RACE, SUSP_SEX) NYCC <- filter(NYCC, JURIS_DESC == “N.Y. POLICE DEPT”) NYCC <- na.omit(NYCC)

NYCC\(Gender <- revalue(NYCC\)SUSP_SEX, c(“M” = “1”,“F” = “2”, “U” = “0” ))

NYCC\(Race <- revalue(NYCC\)SUSP_RACE, c(“UNKNOWN” = “0”, “BLACK” = “1”, “WHITE” = “2”, “BLACK HISPANIC” = “3”, “WHITE HISPANIC” = “4”, “AMER IND” = “5”, “ASIAN/PAC.ISL” = “6”))

NYCC\(Crime_Type <- revalue(NYCC\)LAW_CAT_CD, c(“VIOLATION” = “1”, “MISDEMEANOR” = “2”, “FELONY” = “3”))

NYCC\(Gender <- as.numeric(factor(NYCC\)Gender, levels = c (“1”, “2”, “0”), labels = c(“1”, “2”, “0”)))

NYCC\(Race <- as.numeric(factor(NYCC\)Race, levels = c (“0”, “1”, “2”, “3”, “4”, “5”, “6”), labels = c(“0”, “1”, “2”, “3”, “4”, “5”, “6”)))

NYCC\(Crime_Type <- as.numeric(factor(NYCC\)Crime_Type, levels = c (“1”, “2”, “3”), labels = c(“1”, “2”, “3”)))

view(NYCC)

Created New Variable

‘’’{r echo=TRUE, message=FALSE, paged.print=FALSE} library(knitr) NYCC <- mutate(NYCC, Dept_Boro = paste(JURIS_DESC,BORO_NM, sep = ’_’)) NYCC <- na.omit(NYCC) any(is.na(NYCC)) View(NYCC)

detach(package:plyr)

Summarize

‘’’{r echo=TRUE, message=FALSE, paged.print=FALSE} library(knitr) summarize(NYCC, Crime_Committed = frequency(Crime_Type)

Grouping Variables

‘’’{r echo=TRUE, message=FALSE, paged.print=FALSE} library(knitr)

NYCC <- group_by(NYCC, LAW_CAT_CD, SUSP_RACE) summarize(NYCC, Crime_Type = sum(Crime_Type)) head(NYCC)